#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI

January 31, 2026

AI Summary

5 min read

🎙️ The Voices & The Context

The Format: Casual, technical chat among AI experts, blending deep dives into breakthroughs with forward-looking predictions—accessible yet never dumbed down.
The Key Players:
- Sebastian Rashka: Machine learning researcher, author of Build a Large Language Model (and Reasoning Model) From Scratch—famed for hands-on tutorials that demystify AI by coding from zero.
- Nathan Lambert: Post-training lead at Allen Institute for AI, author of the definitive RLHF book (pre-order live); podcast host and open-source advocate pushing U.S. "truly open" models.
- Host Lex Fridman: Energetic interviewer, weaving in sponsors, personal anecdotes, and big-picture wonder.
The Vibe: Excited and optimistic—educational intensity with laughs, hype over 2025 wins (DeepSeek, Claude), and bold 2026 bets amid U.S.-China rivalry.

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What you'll learn

1 (00:00) **🎙️ Introduction: Sebastian Raschka & Nathan Lambert**
2 (16:29) **DeepSeek Moment & US-China AI Competition**
3 (25:11) **2025 Model Winners & 2026 Predictions**
4 (31:40) **Personal Model Usage & Interfaces**
5 (43:02) **Open-Weight Model Landscape**
6 (52:02) **Architectural Tweaks & Transformer Evolution**
7 (59:38) **Scaling Laws Across Stages**

+ Full timestamped outline available in the app

Show Notes

Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch).
Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep490-sc
See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Transcript:
https://lexfridman.com/ai-sota-2026-transcript

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OUTLINE:
(00:00) – Introduction
(01:39) – Sponsors, Comments, and Reflections
(16:29) – China vs US: Who wins the AI race?
(25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
(36:11) – Best AI for coding
(43:02) – Open Source vs Closed Source LLMs
(54:41) – Transformers: Evolution of LLMs since 2019
(1:02:38) – AI Scaling Laws: Are they dead or still holding?
(1:18:45) – How AI is trained: Pre-training, Mid-training, and Post-training